Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with the PCA (OCC-PCA) model. Such results have confirmed the improved performance achieved by the proposed model.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

News accuracy has been a significant issue for media organizations. However, Arabic errata texts have been neglected in academic research. The purpose of the present study is to identify the generic structural components of errata published in three different types of Arabic texts. We attempt to analyse the generic structure of these texts and determine the linguistic signals used to indicate these components. The paper also sheds light on the different kinds of errors found in Arabic texts and the functions they communicate to readers. For this, we collected and analysed 120 errata from Arabic daily newspapers, academic journals, and popular magazines. For our data analysis, we drew on the genre analysis framework pioneered by Swales (1990) and further adapted by other researchers such as Al-Ali (2005). We additionally referred to Bugeja and Peterson (2007) for classifying the types of errors. The study results show that the analysed Arabic errata texts followed a generic structure comprising eight component moves. Further, objective errors were found to be more frequent than subjective errors in Arabic newspapers and academic journals. We hope that similar future studies further the current understanding of genre identification and construction, especially in Arabic errata texts.